Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy

Nikolaidis, Stefanos; Shah, Julie A.

doi:10.1109/hri.2013.6483499

Cited by 127 publications

(135 citation statements)

References 23 publications

(41 reference statements)

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“…-Chance: This algorithm chooses actions uniformly at random from the set of possible actions. -Mental-model MDP [33]: We follow Nikolaidis et al and define a MDP to formulate the robot's mental model [33]. In this approach, the human actions are incorporated into the state transition function and the policy specifies only the robot's actions.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we use the same state and action spaces and reward function as described in our approach with only one agent and compute the transition function from the state action sequences from the training data. Note that in our adaptation of [33] we fix the transition function learned from the data and do not perform any cross training iterations as the roles are fully exchangeable in our collaborative setting.…”

Section: Methodsmentioning

confidence: 99%

“…After completing each task, the users were asked to rate the following statements on Likert scale from 1 (strongly disagree) to 5 (strongly agree). In this study, we compared the robot policies generated by the mental-model MDP [33] and our method learned with the three human behaviors. Therefore, every user performed each task four times, resulting in a total of 640 ratings.…”

Section: User Study Experiments Setupmentioning

confidence: 99%

“…Some recent works have addressed this problem of collaboration in human-robot teams. Nikolaidis et al [33] consider collaboration for assembling tasks with pre-assigned roles for human and robot. Mainprice et al [27] anticipates human actions to minimize penetration of robot in human workspace.…”

Section: Related Workmentioning

confidence: 99%

“…However, the robotic responses were only reactive and hand-designed. In related works, Nikolaidis and Shah [33] consider collaboration for assembling tasks with pre-assigned roles for human and robot, where they do not explicitly model anticipation. Mainprice and Berenson [27] anticipate human actions to minimize penetration of robot in human workspace and Uyanik et al [44] introduced social-a↵ordances for planning.…”

Section: Introductionmentioning

confidence: 99%

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Anticipatory Planning for Human-Robot Teams

Koppula

Jain

Saxena

2015

Experimental Robotics

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Abstract. When robots work alongside humans for performing collaborative tasks, they need to be able to anticipate human's future actions and plan appropriate actions. The tasks we consider are performed in contextually-rich environments containing objects, and there is a large variation in the way humans perform these tasks. We use a graphical model to represent the state-space, where we model the humans through their low-level kinematics as well as their high-level intent, and model their interactions with the objects through physically-grounded object a↵ordances. This allows our model to anticipate a belief about possible future human actions, and we model the human's and robot's behavior through an MDP in this rich state-space. We further discuss that due to perception errors and the limitations of the model, the human may not take the optimal action and therefore we present robot's anticipatory planning with di↵erent behaviors of the human within the model's scope. In experiments on Cornell Activity Dataset, we show that our method performs better than various baselines for collaborative planning.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: User Study Experiments Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Anticipatory Planning for Human-Robot Teams

Koppula

Jain

Saxena

2015

Experimental Robotics

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Use of cross‐training in human–robot collaborative rescue

Pan,

Zhao,

et al. 2024

Hum Ftrs & Erg Mfg Svc

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Human–robot collaboration has been widely used in postdisaster investigation and rescue. Human–robot team training is a good way to improve the team rescue efficiency and safety; two common training methods, namely, procedural training and cross‐training, are explored in this study. Currently, relatively few studies have explored the impact of cross‐training on human–robot collaboration in rescue tasks. Cross‐training will be novel to most rescuers and as such, an evaluation of cross‐training in comparison with more conventional procedural training is warranted. This study investigated the effects of these two training methods on rescue performance, situation awareness and workload. Forty‐two participants completed a path‐planning and a photo‐taking task in an unfamiliar simulated postdisaster environment. The rescue performance results showed that cross‐training method had significant advantages over procedural training for human–robot collaborative rescue tasks. During the training process, compared with procedural training, participants were more likely to achieve excellent photo‐taking performance after cross‐training; after training, the length of the route planned by the cross‐training group was significantly shorter than that of the procedural‐training group. In addition, procedural‐training marginal significantly increased the emotion demand, which proves that cross‐training can well control the emotions of the operators and make them more involved in the rescue task. The study also found that arousal level increased significantly after the first cross‐training session, and decreased to the same level as procedural training after multiple sessions. These results contribute to the application of cross‐training in human–robot collaborative rescue teams.

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